Data Processing for Device-Free Fine-Grained Occupancy Sensing Using Infrared Sensors

Fine-grained occupancy information plays an essential role for various emerging applications in smart homes, such as personalized thermal comfort control and human behavior analysis. Existing occupancy sensors, such as passive infrared (PIR) sensors generally provide limited coarse information such as motion. However, the detection of fine-grained occupancy information such as stationary presence, posture, identification, and activity tracking can be enabled with the advance of sensor technologies. Among these, infrared sensing is a low-cost, device-free, and privacy-preserving choice that detects the fluctuation (PIR sensors) or the thermal profiles (thermopile array sensors) from objects' infrared radiation. This work focuses on developing data processing models towards fine-grained occupancy sensing using the synchronized low-energy electronically chopped PIR (SLEEPIR) sensor or the thermopile array sensors. The main contributions of this dissertation include: (1) creating and validating the mathematical model of the SLEEPIR sensor output towards stationary occupancy detection; (2) developing the SLEEPIR detection algorithm using statistical features and long-short term memory (LSTM) deep learning; (3) building machine learning framework for posture detection and activity tracking using thermopile array sensors; and (4) creating convolutional neural network (CNN) models for facing direction detection and identification using thermopile array sensors

Analyzing WiFi connection counts in commercial/institutional buildings to estimate/predict occupancy patterns for optimizing buildings’ systems operation

Accurate occupancy information can help in optimizing the operation of building systems. To obtain this information, previous studies suggested using WiFi connection counts due to their strong correlation with occupancy counts. However, validating this correlation and investigating its variation have remained limited due to challenges regarding collecting ground-truth data. Moreover, the difficulty of integrating real-time WiFi traffic data in building automation systems hinders wide-scale deployment of this approach. This research addressed these gaps by proposing a methodology, including two modules focused on developing frameworks, for (i) validating the correlation between WiFi connection counts and actual building occupancy counts by using continuous ground-truth data collected from camera-based occupancy counters; and (ii) extracting occupancy indicators from WiFi connection count data which can then be used for updating control sequences. The proposed research was implemented in two institutional buildings to validate the proposed methods in two case studies. Results of the first case study showed Hour of the day, Day of the week, as well as occupancy level, affect the correlation between WiFi and occupancy counts. Furthermore, the proposed models could successfully estimate real-time occupancy counts and predict day-ahead occupancy counts with an average accuracy (R2) of 0.97 and 0.87, respectively. Moreover, the results of the second case study revealed that the proposed models could successfully predict weekly building occupancy patterns, with an average accuracy (RD2) of 0.90. Furthermore, the analysis identified peak occupancy timing, as well as arrival/departure times variations between different zones. These findings provided a proof-of-concept for the proposed methodology and demonstrated the potential of using WiFi connection count for estimating/forecasting occupancy counts at a large scale and extracting actionable information to optimize buildings’ system operation based on buildings’ unique occupancy patterns